Enhanced spectrum sensing based on Cyclo-stationary Feature Detection (CFD) in cognitive radio network using Fixed &amp;amp; Dynamic Thresholds Levels

Patil, Prajwal; Pawar, Pradeep R; Jain, Praneeth P; Manoranjan, K; Pradhan, Devasis

doi:10.36348/sjet.2020.v05i06.003

Cited by 5 publications

(4 citation statements)

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“…The data AP is a gadget in the framework based remote organization that all data signals are sent and gotten to/from it. This AP is utilized as the doorway to the Internet [21,22]. The power AP is a power transmitter furnished with a steady power source to send capacity to IDs.…”

Section: Green Wpc Architecture Using Iotmentioning

confidence: 99%

Green WPC: Energy Harvesting in Smart Cities

Pradhan¹,

Tun²,

Naing³

et al. 2022

Middle East Res J Engr Technol

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Ongoing advances in Wireless Power Transmission (WPT) innovation give an alluring arrangement, in particular Wireless Powered Communication (WPC), in which remote gadgets are charged through remote power transmitters. In any case, the plan and prospect of WPC applications are tested by the unfortunate proficiency of significant distance WPT. This article give a review of WPT innovation as well as sun based energy gathering and explores their difficulties in IoT networks. Thus, the world is seeing a restored consciousness of the significance of IoT gadgets, as basic parts of smart cities, particularly for the pith of endurance notwithstanding lockdown. Energy gathering is a potential arrangement that could empower IoT hubs to search self-supporting energy from ecological surrounding sources.

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Section: Green Wpc Architecture Using Iotmentioning

confidence: 99%

Green WPC: Energy Harvesting in Smart Cities

Pradhan¹,

Tun²,

Naing³

et al. 2022

Middle East Res J Engr Technol

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“…Simultaneously, the miss detection probability was found to be 0.071, signifying a relatively low rate of failing to detect the occupied spectrum when the false alarm rate was set at 0.04. Patil et al (2020) detailed a technique for identifying the existence of the primary signal within the channel. This method relies on analyzing the periodic statistical properties and spectral correlation of the received signal, a concept known as "cyclostationary".…”

Section: Introductionmentioning

confidence: 99%

A Spectrum Sensing and Allocation Model for Primary User Detection and Interference Mitigation in Television Whitespaces

Notcker,

Adetiba,

Adetiba

et al. 2024

Journal of Computer Science

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Television White Space (TVWS) emerges as an encouraging solution to address the challenge of a restricted wireless communication spectrum. It denotes the frequency range spanning from 54-790 MHz and researchers have increasingly explored its propagation characteristics in recent years. Nonetheless, a notable hindrance to its effective utilization lies in the interference between primary and secondary users, as well as interference among secondary users themselves. Approaches involving spectrum sensing and resource allocation have been extensively employed independently to tackle these issues, yet they have not been integrated or utilized in combination. Hence, in this study, we formulated an architectural model that combines spectrum sensing and allocation components. This integrated model aims to detect the presence of primary users while simultaneously minimizing interference among secondary users. The spectrum sensing component utilized an energy detection model to identify primary users, mitigating interference with secondary users. Meanwhile, the spectrum allocation component employed the Particle Swarm Optimization (PSO) algorithm to determine the optimal distribution of channels among secondary users. We implemented the architectural model in a simulated TVWS network using MATLAB R2020a. Its performance was then evaluated and compared with that of matched filter and Artificial Bee Colony (ABC) algorithms, which were utilized for spectrum sensing and allocation, respectively. Based on the simulation findings, when the Signal-to-Noise Ratio (SNR) was configured at -10 dB, the detection probability for the energy detection model reached 98.23%, surpassing the matched filter's detection probability of 92.55%. With a false alarm probability of 0.51, the energy detection model exhibited a misdetection probability of 0.13%, outperforming the matched filter which had a higher misdetection probability of 2.61%. In scenarios with 10 channels and 100 secondary users, the particle swarm optimization algorithm attained a maximum throughput of 279.9 Mbps, slightly outperforming the artificial bee colony algorithm, which achieved 278.7 Mbps. In scenarios with 30 channels and 200 secondary users, the particle swarm optimization algorithm achieved throughputs of 1.575 Gbps, whereas the artificial bee colony algorithm achieved a comparable throughput of 1.571 Gbps. In the scenario where the number of channels was set to 50 and users to 300, the particle swarm optimization algorithm achieved a throughput of 3.879 Gbps, slightly surpassing the artificial bee colony algorithm, which achieved 3.864 Gbps. While the designed components consistently outperformed the matched filter and artificial bee colony algorithms across all cases, it's important to note that the model faced limitations. Specifically, it was unable to detect more than one primary user or allocate spectrum for a new incoming secondary user.

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“…The decision variable is the output of the device for integrating and squaring, which is used to locate energy. The threshold is then compared to the decision variable [5][6][7]. The detector's output is sent to a primary user if the decision variable is greater than the threshold.…”

Section: Introductionmentioning

confidence: 99%

4G/VoLTE- Spectrum Sensing using Cyclostationary Detection (CS-SS) Method: A Review

Kumari¹,

Shrishti²,

Jain³

2023

Middle East Res J Engr Technol

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Artificial Neural Networks (ANNs), Deep Learning, and AI Deep learning is a subset of machine learning. In order to create pictures with a greater resolution, a high-resolution GAN combines a deep network with an opponent network. An approach to generative modelling that uses deep learning techniques like convolutional neural networks is known as generative adversarial networks, or GAN. In the Super-Resolution Generative Adversarial Network (SRGAN), a Generative Adversarial Network (GAN) may transform low-resolution images into super resolution images that are more finely detailed and of higher quality. In the past, CNNs were used to produce incredibly precise and detailed images. However, they could have trouble recalling specifics and usually draw hazy visuals which can be overcome by SRGAN.

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Enhanced spectrum sensing based on Cyclo-stationary Feature Detection (CFD) in cognitive radio network using Fixed & Dynamic Thresholds Levels

Cited by 5 publications

References 0 publications

Green WPC: Energy Harvesting in Smart Cities

Green WPC: Energy Harvesting in Smart Cities

A Spectrum Sensing and Allocation Model for Primary User Detection and Interference Mitigation in Television Whitespaces

4G/VoLTE- Spectrum Sensing using Cyclostationary Detection (CS-SS) Method: A Review

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